Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning

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Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this paper is how to scale machine learning algorithms, that typically are designed to deal with main memory based datasets, to efficiently learn from large distributed databases. We have explored an approach called meta-learning that is related to the traditional approaches of data reduction commonly employed in distributed query processing systems. Here we seek efficient means to learn how to combine a number of base classifiers, which are learned from subsets of the data, so that we scale efficiently to larger learning problems, and boost the accuracy of the constituent classifiers if possible. In this paper we compare the arbiter tree strategy to a new but related approach called the combiner tree strategy.